openCV 图像基本操作（二）


opencv img 转为jpg字节流 opencv img.shape_边缘检测


opencv img 转为jpg字节流 opencv img.shape_opencv img 转为jpg字节流_02


#!/usr/bin/env python
# coding: utf-8

# ### 灰度图

# In[1]:


import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
get_ipython().run_line_magic('matplotlib', 'inline')

img=cv2.imread('cat.jpg')
img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_gray.shape


# In[2]:


cv2.imshow("img_gray", img_gray)
cv2.waitKey(0) 
cv2.destroyAllWindows() 


# ### HSV
# - H - 色调（主波长）。 
# - S - 饱和度（纯度/颜色的阴影）。 
# - V值（强度）

# In[3]:


hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

cv2.imshow("hsv", hsv)
cv2.waitKey(0)    
cv2.destroyAllWindows()


# ### 图像阈值
# 
# #### ret, dst = cv2.threshold(src, thresh, maxval, type)
# 
# - src： 输入图，只能输入单通道图像，通常来说为灰度图
# - dst： 输出图
# - thresh： 阈值
# - maxval： 当像素值超过了阈值（或者小于阈值，根据type来决定），所赋予的值
# - type：二值化操作的类型，包含以下5种类型： cv2.THRESH_BINARY； cv2.THRESH_BINARY_INV； cv2.THRESH_TRUNC； cv2.THRESH_TOZERO；cv2.THRESH_TOZERO_INV
# 
# - cv2.THRESH_BINARY           超过阈值部分取maxval（最大值），否则取0
# - cv2.THRESH_BINARY_INV    THRESH_BINARY的反转
# - cv2.THRESH_TRUNC            大于阈值部分设为阈值，否则不变
# - cv2.THRESH_TOZERO          大于阈值部分不改变，否则设为0
# - cv2.THRESH_TOZERO_INV  THRESH_TOZERO的反转
# 
# 

# In[4]:


ret, thresh1 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO)
ret, thresh5 = cv2.threshold(img_gray, 127, 255, cv2.THRESH_TOZERO_INV)

titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in range(6):
    plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')
    plt.title(titles[i])
    plt.xticks([]), plt.yticks([])
plt.show()


# ### 图像平滑

# ![image.png](attachment:image.png)

# In[5]:


img = cv2.imread('lenaNoise.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[6]:


# 均值滤波
# 简单的平均卷积操作
blur = cv2.blur(img, (3, 3))

cv2.imshow('blur', blur)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[7]:


# 方框滤波
# 基本和均值一样，可以选择归一化
box = cv2.boxFilter(img,-1,(3,3), normalize=True)  # normalize： 表示是否进行归一化，进行归一化，其效果与均值滤波一样

cv2.imshow('box', box)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[8]:


# 方框滤波
# 基本和均值一样，可以选择归一化,容易越界
box = cv2.boxFilter(img,-1,(3,3), normalize=False)  # 其一旦越界，会取255

cv2.imshow('box', box)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[9]:


# 高斯滤波
# 高斯模糊的卷积核里的数值是满足高斯分布，相当于更重视中间的
aussian = cv2.GaussianBlur(img, (5, 5), 1)  

cv2.imshow('aussian', aussian)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[10]:


# 中值滤波
# 相当于用中值代替
median = cv2.medianBlur(img, 5)  # 中值滤波

cv2.imshow('median', median)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[11]:


# 展示所有的
res = np.hstack((blur,aussian,median))
# res = np.hstack((blur,aussian,median))
#print (res)
cv2.imshow('median vs average', res)
cv2.imwrite("res_11.png",res)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](res_11.png)

# ### 形态学-腐蚀操作

# In[12]:


img = cv2.imread('dige.png')

cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[13]:


kernel = np.ones((3,3),np.uint8) 
erosion = cv2.erode(img,kernel,iterations = 1)

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[14]:


pie = cv2.imread('pie.png')

cv2.imshow('pie', pie)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[15]:


kernel = np.ones((30,30),np.uint8) 
erosion_1 = cv2.erode(pie,kernel,iterations = 1)
erosion_2 = cv2.erode(pie,kernel,iterations = 2)
erosion_3 = cv2.erode(pie,kernel,iterations = 3)
res = np.hstack((erosion_1,erosion_2,erosion_3))
cv2.imshow('res', res)
cv2.imwrite("res_0.png",res)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](res_0.png)

# ### 形态学-膨胀操作

# In[16]:


img = cv2.imread('dige.png')
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[17]:


kernel = np.ones((3,3),np.uint8) 
dige_erosion = cv2.erode(img,kernel,iterations = 1)

cv2.imshow('erosion', erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[18]:


kernel = np.ones((3,3),np.uint8) 
dige_dilate = cv2.dilate(dige_erosion,kernel,iterations = 1)

cv2.imshow('dilate', dige_dilate)
cv2.waitKey(0)
cv2.destroyAllWindows()


# In[19]:


pie = cv2.imread('pie.png')

kernel = np.ones((30,30),np.uint8) 
dilate_1 = cv2.dilate(pie,kernel,iterations = 1)
dilate_2 = cv2.dilate(pie,kernel,iterations = 2)
dilate_3 = cv2.dilate(pie,kernel,iterations = 3)
res = np.hstack((dilate_1,dilate_2,dilate_3))
cv2.imshow('res', res)
cv2.imwrite("res_16.png",res)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](res_16.png)

# ### 开运算与闭运算

# In[20]:


# 开：先腐蚀，再膨胀
img = cv2.imread('dige.png')

kernel = np.ones((5,5),np.uint8) 
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

cv2.imshow('opening', opening)
cv2.imwrite("opening.png",opening)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](opening.png)

# In[21]:


# 闭：先膨胀，再腐蚀
img = cv2.imread('dige.png')

kernel = np.ones((5,5),np.uint8) 
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

cv2.imshow('closing', closing)
cv2.imwrite("closing.png",closing)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](closing.png)

# ### 梯度运算

# In[22]:


# 梯度=膨胀-腐蚀
pie = cv2.imread('pie.png')
kernel = np.ones((7,7),np.uint8) 
dilate = cv2.dilate(pie,kernel,iterations = 5)   # 进行膨胀处理
erosion = cv2.erode(pie,kernel,iterations = 5)   # 进行腐蚀处理

res = np.hstack((dilate,erosion))

cv2.imshow('res', res)
cv2.imwrite("res_2.png",res)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](res_2.png)

# In[23]:


gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel)  # 计算图像的梯度 

cv2.imshow('gradient', gradient)
cv2.imwrite("gradient.png",gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](gradient.png)

# ### 礼帽与黑帽
# - 礼帽 = 原始输入-开运算结果    
# - 黑帽 = 闭运算-原始输入

# In[24]:


#礼帽
img = cv2.imread('dige.png')
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
cv2.imshow('tophat', tophat)
cv2.imwrite("tophat.png",tophat)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](tophat.png)

# In[25]:


#黑帽
img = cv2.imread('dige.png')
blackhat  = cv2.morphologyEx(img,cv2.MORPH_BLACKHAT, kernel)
cv2.imshow('blackhat ', blackhat )
cv2.imwrite("blackhat.png",blackhat)
cv2.waitKey(0)
cv2.destroyAllWindows()


# ![title](blackhat.png)

# ### 图像梯度-Sobel算子

# ![title](sobel_1.png)

# In[26]:


img = cv2.imread('pie.png',cv2.IMREAD_GRAYSCALE)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()


# dst = cv2.Sobel(src, ddepth, dx, dy, ksize)
# - ddepth:图像的深度    通常设置为-1
# - dx和dy分别表示水平和竖直方向
# - ksize是Sobel算子的大小
# 

# In[27]:


def cv_show(img,name):
    cv2.imshow(name,img)
    cv2.imwrite(name+".png",img)
    cv2.waitKey()
    cv2.destroyAllWindows()


# In[28]:


sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)    # cv2.CV_64F：可以表示负数的形式

cv_show(sobelx,'sobelx')


# ![title](sobelx.png)

# 白到黑是正数，黑到白就是负数了，所有的负数会被截断成0，所以要取绝对值

# In[29]:


sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)# 取绝对值操作
cv_show(sobelx,'sobelx')


# ![title](sobelx.png)

# In[30]:


sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = cv2.convertScaleAbs(sobely)  
cv_show(sobely,'sobely')


# ![title](sobely.png)

# 分别计算x和y，再求和

# In[31]:


sobelxy = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
cv_show(sobelxy,'sobelxy')


# ![title](sobelxy.png)

# 不建议直接计算

# In[32]:


sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)
sobelxy = cv2.convertScaleAbs(sobelxy) 
cv_show(sobelxy,'sobelxy')


# In[33]:


img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
cv_show(img,'img')


# In[34]:


img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobely = cv2.convertScaleAbs(sobely)
sobelxy_ = cv2.addWeighted(sobelx,0.5,sobely,0.5,0)
cv_show(sobelxy_,'sobelxy_')


# ![title](sobelxy_.png)

# img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
# 
# sobelxy=cv2.Sobel(img,cv2.CV_64F,1,1,ksize=3)
# sobelxy = cv2.convertScaleAbs(sobelxy) 
# cv_show(sobelxy,'sobelxy')

# ### 图像梯度-Scharr算子

# ![title](scharr.png)

# ### 图像梯度-laplacian算子

# ![title](l.png)

# In[35]:


#不同算子的差异
img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=3)
sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=3)
sobelx = cv2.convertScaleAbs(sobelx)   
sobely = cv2.convertScaleAbs(sobely)  
sobelxy =  cv2.addWeighted(sobelx,0.5,sobely,0.5,0)  

scharrx = cv2.Scharr(img,cv2.CV_64F,1,0)
scharry = cv2.Scharr(img,cv2.CV_64F,0,1)
scharrx = cv2.convertScaleAbs(scharrx)   
scharry = cv2.convertScaleAbs(scharry)  
scharrxy =  cv2.addWeighted(scharrx,0.5,scharry,0.5,0) 

laplacian = cv2.Laplacian(img,cv2.CV_64F)
laplacian = cv2.convertScaleAbs(laplacian)   

res_10 = np.hstack((sobelxy,scharrxy,laplacian))
cv_show(res_10,'res_10')


# ![title](res_10.png)

# In[36]:


img = cv2.imread('lena.jpg',cv2.IMREAD_GRAYSCALE)
cv_show(img,'img')


# ### Canny边缘检测
# - 1)        使用高斯滤波器，以平滑图像，滤除噪声。
# 
# - 2)        计算图像中每个像素点的梯度强度和方向。
# 
# - 3)        应用非极大值（Non-Maximum Suppression）抑制，以消除边缘检测带来的杂散响应。
# 
# - 4)        应用双阈值（Double-Threshold）检测来确定真实的和潜在的边缘。
# 
# - 5)        通过抑制孤立的弱边缘最终完成边缘检测。

# #### 1:高斯滤波器

# ![title](canny_1.png)

# #### 2:梯度和方向

# ![title](canny_2.png)

# #### 3：非极大值抑制

# ![title](canny_3.png)

# ![title](canny_6.png)

# #### 4：双阈值检测

# ![title](canny_5.png)

# In[37]:


img=cv2.imread("lena.jpg",cv2.IMREAD_GRAYSCALE)

v1=cv2.Canny(img,80,150)   # 80与150分别是minVal和maxVal
v2=cv2.Canny(img,50,100)

res_v1v2 = np.hstack((v1,v2))
cv_show(res_v1v2,'res_v1v2')


# ![title](res_v1v2.png)

# In[38]:


img=cv2.imread("car.png",cv2.IMREAD_GRAYSCALE)

v1=cv2.Canny(img,120,250)
v2=cv2.Canny(img,50,100)

res = np.hstack((v1,v2))
cv2.imwrite("result1.png",res)
cv_show(res,'res')


# ![title](result1.png)

# ### 图像金字塔
# - 高斯金字塔
# - 拉普拉斯金字塔

# ![title](Pyramid_1.png)

# #### 高斯金字塔：向下采样方法（缩小）

# ![title](Pyramid_2.png)

# #### 高斯金字塔：向上采样方法（放大）

# ![title](Pyramid_3.png)

# In[39]:


img=cv2.imread("AM.png")
cv_show(img,'img')
print (img.shape)


# In[40]:


up=cv2.pyrUp(img)   # 其进行上采样
cv_show(up,'up')
print (up.shape)


# ![title](up.png)

# In[41]:


down=cv2.pyrDown(img)    # 进行下采样
cv_show(down,'down')
print (down.shape)


# ![title](down.png)

# In[42]:


up2=cv2.pyrUp(up)
cv_show(up2,'up2')
print (up2.shape)


# In[43]:


up=cv2.pyrUp(img)
up_down=cv2.pyrDown(up)
cv_show(up_down,'up_down')


# In[44]:


cv_show(np.hstack((img,up_down)),'up_down_')


# ![title](up_down_.png)

# In[45]:


up=cv2.pyrUp(img)
up_down=cv2.pyrDown(up)
cv_show(img-up_down,'img-up_down')


# ![title](img-up_down.png)

# #### 拉普拉斯金字塔

# ![title](Pyramid_4.png)

# In[46]:


down=cv2.pyrDown(img)
down_up=cv2.pyrUp(down)
l_1=img-down_up     # 拉普拉斯金字塔操作
cv_show(l_1,'l_1')


# ![title](l_1.png)

# ### 图像轮廓

# #### cv2.findContours(img,mode,method)
# mode:轮廓检索模式
# - RETR_EXTERNAL ：只检索最外面的轮廓；
# - RETR_LIST：检索所有的轮廓，并将其保存到一条链表当中；
# - RETR_CCOMP：检索所有的轮廓，并将他们组织为两层：顶层是各部分的外部边界，第二层是空洞的边界;
# - RETR_TREE：检索所有的轮廓，并重构嵌套轮廓的整个层次;
# 
# method:轮廓逼近方法
# - CHAIN_APPROX_NONE：以Freeman链码的方式输出轮廓，所有其他方法输出多边形（顶点的序列）。
# - CHAIN_APPROX_SIMPLE:压缩水平的、垂直的和斜的部分，也就是，函数只保留他们的终点部分。

# ![title](chain.png)

# 为了更高的准确率，使用二值图像。

# In[47]:


img = cv2.imread('contours.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
cv_show(thresh,'thresh')


# ![title](thresh.png)

# In[48]:


contours,binary = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)


# 绘制轮廓

# In[49]:


cv_show(img,'img')


# In[50]:


#传入绘制图像，轮廓，轮廓索引，颜色模式，线条厚度
# 注意需要copy,要不原图会变。。。
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2)
cv_show(res,'res_draw')


# ![title](res_draw.png)

# In[51]:


draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, 0, (0, 0, 255), 2)
cv_show(res,'res_one')


# ![title](res_one.png)

# #### 轮廓特征

# In[52]:


cnt = contours[0]


# In[53]:


#面积
cv2.contourArea(cnt)


# In[54]:


#周长，True表示闭合的
cv2.arcLength(cnt,True)


# #### 轮廓近似

# ![title](contours3.png)

# In[55]:


img = cv2.imread('contours2.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

draw_img = img.copy()
res = cv2.drawContours(draw_img, [cnt], -1, (0, 0, 255), 2)
cv_show(res,'res_draw_1')


# ![title](res_draw_1.png)

# In[56]:


epsilon = 0.15*cv2.arcLength(cnt,True) 
approx = cv2.approxPolyDP(cnt,epsilon,True)

draw_img = img.copy()
res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)
cv_show(res,'res_approx')


# ![title](res_approx.png)

# 边界矩形

# In[57]:


img = cv2.imread('contours.png')

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]

x,y,w,h = cv2.boundingRect(cnt)
img = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv_show(img,'img_boundingRect')


# ![title](img_boundingRect.png)

# In[58]:


area = cv2.contourArea(cnt)    # 计算轮廓面积
x, y, w, h = cv2.boundingRect(cnt)
rect_area = w * h
extent = float(area) / rect_area
print ('轮廓面积与边界矩形比',extent)


# 外接圆

# In[59]:


(x,y),radius = cv2.minEnclosingCircle(cnt) 
center = (int(x),int(y)) 
radius = int(radius) 
img = cv2.circle(img,center,radius,(0,255,0),2)
cv_show(img,'img_circle')


# ![title](img_circle.png)

# ### 傅里叶变换

# 我们生活在时间的世界中，早上7:00起来吃早饭，8:00去挤地铁，9:00开始上班。。。以时间为参照就是时域分析。
# 
# 但是在频域中一切都是静止的！
# 
# https://zhuanlan.zhihu.com/p/19763358
# 

# ### 傅里叶变换的作用
# 
# - 高频：变化剧烈的灰度分量，例如边界
# 
# - 低频：变化缓慢的灰度分量，例如一片大海
# 
# ### 滤波
# 
# - 低通滤波器：只保留低频，会使得图像模糊
# 
# - 高通滤波器：只保留高频，会使得图像细节增强
# 
# 

# - opencv中主要就是cv2.dft()和cv2.idft()，输入图像需要先转换成np.float32 格式。
# - 得到的结果中频率为0的部分会在左上角，通常要转换到中心位置，可以通过shift变换来实现。
# - cv2.dft()返回的结果是双通道的（实部，虚部），通常还需要转换成图像格式才能展示（0,255）。

# In[60]:


import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('lena.jpg',0)

img_float32 = np.float32(img)

dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)   # 将低频的值转换到中间的位置
# 得到灰度图能表示的形式
magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))# 将结果映射到0~255之间，转换成图像的格式

plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()


# In[61]:


import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('lena.jpg',0)

img_float32 = np.float32(img)

dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)   # 将低频的值转换到中间的位置

rows, cols = img.shape
crow, ccol = int(rows/2) , int(cols/2)     # 中心位置

# 低通滤波
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 1

# IDFT
fshift = dft_shift*mask
f_ishift = np.fft.ifftshift(fshift)   # 将中间位置的信号恢复至边缘位置
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:,:,0],img_back[:,:,1])    # 将是实部信号与虚部信号进行处理

plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img_back, cmap = 'gray')
plt.title('Result'), plt.xticks([]), plt.yticks([])

plt.show()                


# In[62]:


img = cv2.imread('lena.jpg',0)

img_float32 = np.float32(img)

dft = cv2.dft(img_float32, flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)

rows, cols = img.shape
crow, ccol = int(rows/2) , int(cols/2)     # 中心位置

# 高通滤波（即只保留边界的一些信息，去除掉了细节信息）
mask = np.ones((rows, cols, 2), np.uint8)
mask[crow-30:crow+30, ccol-30:ccol+30] = 0

# IDFT
fshift = dft_shift*mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:,:,0],img_back[:,:,1])

plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(img_back, cmap = 'gray')
plt.title('Result'), plt.xticks([]), plt.yticks([])

plt.show()